import numpy as np
from math import sqrt
import  matplotlib.pyplot as plt
import  warnings
from matplotlib import  style
from collections import  Counter
style.use('fivethirtyeight')

dataset = {'k':[[1,2],[2,3],[3,1]],'r':[[6,5],[7,7],[8,6]]}
new_features=[3,6]



def k_nearest_neighbors(data,predict,k=3):
    if len(data) >= k:
        warnings.warn('K is set to a value less than total voting groups!')
    distances = []
    votes=[]
    for group in data:
        for features in data[group]:
            euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))
            distances.append([euclidean_distance,group])
    print distances
    print sorted(distances)

    for i in sorted(distances)[:k]:
        votes.append(i[1])

    print (Counter(votes))
    print (Counter(votes)).most_common(1)
    vote_result= Counter(votes).most_common(1)[0][0]

    return vote_result

result=k_nearest_neighbors(dataset,new_features,k=3)
print result


for i in dataset:
    for ii in dataset[i]:
        print ii
        plt.scatter(ii[0],ii[1],s=100,color=i)
plt.scatter(new_features[0],new_features[1],s=100,color=result)
plt.show()